joseph.ergo@proton.me | Portfolio | Resume PDF | Linked-In | +212 713-617-633

Available immediately for full/part-time remote roles

SMILE OPEN SOURCE SOLUTIONS

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## SETUP
from pathlib import Path
import duckdb
from tqdm.notebook import tqdm
import datetime
import copy
import polars as pl
import plotly.express as px
import plotly.io as pio
import re
from concurrent.futures import ThreadPoolExecutor
import plotly.graph_objects as go
import networkx as nx
import numpy as np
# pio.renderers.default = 'plotly_mimetype'
pio.renderers.default = 'jupyterlab+notebook'
pio.templates.default = "plotly_white"

path_data = Path.cwd()/'data'/'03_rdb'
path_data_companies = path_data/'companies_table.parquet'
path_data_experience = path_data/'experience_table.parquet'
path_data_emails = path_data/'emails_table.parquet'
path_data_education = path_data/'education_table.parquet'
path_data_school = path_data/'school_table.parquet'
path_data_persona = path_data/'persona_table.parquet'
path_data_profiles = path_data/'profiles_table.parquet'

path_output_images = Path.cwd()/'output'/'images'

conn = duckdb.connect()

conn.execute("SET temp_directory = 'temp';")
conn.execute("SET memory_limit = '10GB';")
conn.execute("SET max_temp_directory_size = '100GB';")
conn.execute("SET threads = 8;")
conn.execute("SET preserve_insertion_order = false;")
conn.execute("SET enable_progress_bar = true;")
conn.execute("SET enable_progress_bar_print = true;")

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df = pl.read_parquet('03_target_companies3.parquet')
df_yearly_new_hires_per_indestry = pl.read_parquet('03_yearly_new_hires_per_indestry.parquet')

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current_company_id = "&-friends"
current_company_id = pl.read_json("04__control__.json")[0,'current_company_id']
query = f"""
SELECT *
FROM read_parquet('{path_data_companies}')
WHERE company_id = '{current_company_id}'
"""
df_company_by_company_id = pl.DataFrame(conn.execute(query).df())

current_company_name = df_company_by_company_id[0,'company_name']
current_company_indestry = df_company_by_company_id[0,'company_industry']

current_company_parquet = Path.cwd()/'output'/'company_data'/f"{current_company_id}.parquet"

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# Info about personas status from company_id

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query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE company_id = '{current_company_id}'
"""
df_experiences_by_company_id = pl.DataFrame(conn.execute(query).df())

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personas_whitout_end_date = df_experiences_by_company_id.filter(pl.col('end_date').is_null())
personas_who_got_raise = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) &
                                     pl.col('persona_id').is_in(personas_whitout_end_date['persona_id'].to_list()))
personas_who_stayed = (pl
                      .concat([personas_whitout_end_date, personas_who_got_raise])
                      .sort('start_date')
                      .group_by('persona_id')
                      .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                      )
                      .with_columns(
                          pl.lit(True).alias('still_associated'),
                          pl.lit(None).alias('end_date')
                      )
                      .sort('changes')
                             )

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personas_who_left = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) & ~pl.col('persona_id').is_in(personas_who_stayed['persona_id'].to_list()) )
personas_who_left = (personas_who_left
                     .sort('start_date')
                     .group_by('persona_id')
                     .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                              )
                     .with_columns(
                         pl.lit(False).alias('still_associated'),
                         
                     )
                     .sort('changes'))

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df_personas_who_worked_in_company = pl.concat([personas_who_stayed, personas_who_left], how='vertical_relaxed').with_columns(
    (pl.col('end_date').dt.year()-pl.col('start_date').dt.year()).alias('work_durration')
).sort('work_durration')

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import dns.resolver
import smtplib
import socket

def check_deliverability(email_address):
    """
    Checks the deliverability of an email address by verifying MX records
    and performing an SMTP connection test.
    """
    if '@' not in email_address:
        return False
    
    domain = email_address.split('@')[1]
    
    # Check for MX records
    try:
        mx_records = dns.resolver.resolve(domain, 'MX')
        if not mx_records:
            return False
    except (dns.resolver.NoAnswer, dns.resolver.NXDOMAIN, dns.resolver.Timeout):
        return False

    # Perform SMTP connection test
    mx_host = str(mx_records[0].exchange)
    
    # Validate MX hostname before attempting connection
    try:
        # Test if hostname can be properly encoded
        mx_host.encode('idna')
    except UnicodeError:
        return False
    
    try:
        with smtplib.SMTP(mx_host, timeout=10) as smtp:
            smtp.set_debuglevel(0)
            smtp.helo(socket.gethostname())
            smtp.mail('test@example.com')
            code, _ = smtp.rcpt(email_address)

            return code == 250  # 250 indicates valid email address
            
    except (smtplib.SMTPException, socket.error, UnicodeError):
        return False

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# info of all personas info
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_persona}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas = pl.DataFrame(conn.execute(query).df())

# info of all personas profiles
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_profiles}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_profile = pl.DataFrame(conn.execute(query).df())
df_all_personas_profile_f = df_all_personas_profile.group_by('persona_id').agg(pl.col('url').unique())

# info of all personas email
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_emails}')
WHERE persona_id IN ({list_for_in}) AND type == 'personal'
"""
df_all_personas_emails = pl.DataFrame(conn.execute(query).df())

def def_polars_fix_gmail(x):
    if "@gmail" in x:
        first_part = x.split('@')[0]
        second_part = x.split('@')[1]
        return f"{first_part.replace(".",'')}@{second_part}"
    else:
        return x

df_all_personas_emails_f = (df_all_personas_emails
                            .with_columns(pl.col('address')
                                          .map_elements(def_polars_fix_gmail, return_dtype=pl.String)
                                          .alias('normalised_emails'))
                            .unique('normalised_emails', keep='first')
                            .sort('persona_id')
                            .drop('normalised_emails')
                         )
df_all_personas_emails_f = (df_all_personas_emails_f.group_by('persona_id').agg(pl.col('address').unique(),pl.col('type').unique()))
df_all_personas_plus = df_all_personas.join(df_all_personas_emails_f, on='persona_id', how='left')

df_full_personas_who_worked_in_company = (df_personas_who_worked_in_company
                                       .join(df_all_personas_plus, on='persona_id', how='left')
                                       .join(df_all_personas_profile_f, on='persona_id', how='left')
                                      )

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        (pl.col("start_date").fill_null(pl.col("start_date").min()))
        .dt.year()
        .alias("start_year"),
        (pl.col("end_date").dt.year()).alias("end_year"),
    )
)

work_years = []
for i in range(len(df_full_personas_who_worked_in_company)):
    start_y = df_full_personas_who_worked_in_company[i, "start_year"]
    if df_full_personas_who_worked_in_company[i, "end_year"]:
        end_y = df_full_personas_who_worked_in_company[i, "end_year"]
    else:
        end_y = 2020

    tmp_work_years = []
    for y in range(start_y, end_y + 1):
        tmp_work_years.append(y)

    work_years.append(tmp_work_years)

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        pl.Series("work_years", work_years)
    )
)

# add hireups
title_name_match = ["ceo","chief","founder","owner","president","vp","vice","director",
    "cfo","cto","partner","head of","hr ","human","talent","senior","manager","lead"]

df_full_personas_who_worked_in_company = (df_full_personas_who_worked_in_company
    .with_columns(
        pl.when(pl.col('title_name').str.contains_any(title_name_match)).then(True).otherwise(False).alias("higher_up")
    ))



df_tmp_email_checker = (
    df_full_personas_who_worked_in_company
    .filter(
            pl.col('still_associated')==True,
            pl.col('address').list.len()>0
    )
        ['persona_id','address']
        .explode('address')
)

# if current_company_parquet.exists():
#     df_pre_full_personas_who_worked_in_company = pl.read_parquet(current_company_parquet)
#     list_pre_deliverable_address = df_pre_full_personas_who_worked_in_company['address'].drop_nulls().explode().to_list()
# else:
#     list_pre_deliverable_address = []

# list_of_emails_to_check = df_tmp_email_checker['address'].drop_nulls().to_list()
# list_lists_email_check = []

# var_total_emails = len(list_of_emails_to_check)
# var_current_email_count = 0

# def def_check_and_populate(email_to_check):
#     global list_lists_email_check, var_current_email_count
#     if email_to_check in list_pre_deliverable_address:
#         list_lists_email_check.append([email_to_check, True])
#     elif '@gmail' in email_to_check:
#         list_lists_email_check.append([email_to_check, True])
#     else:
#         try:
#             is_deliverable = check_deliverability(email_to_check)
#             list_lists_email_check.append([email_to_check, is_deliverable])
#         except:
#             list_lists_email_check.append([email_to_check, False])
#     var_current_email_count += 1
#     print(' '*10, end='\r')
#     print(round(var_current_email_count/var_total_emails,5), end='\r')

# with ThreadPoolExecutor(max_workers=20) as executor:
#     results = list(executor.map(def_check_and_populate, list_of_emails_to_check))

# df_email_check = pl.DataFrame(list_lists_email_check, schema=["address", "deliverable"], orient="row")
# try:
#     df_tmp_email_checker_f = (
#         df_tmp_email_checker
#             .join(df_email_check, on='address')
#             .filter(pl.col('deliverable')==True)
#             .group_by('persona_id').agg(pl.col('address').unique().alias("deliverable_address"))
#     )
# except:
#     df_tmp_email_checker_f = pl.DataFrame()

# if df_tmp_email_checker_f.is_empty():
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker.rename({'address':'deliverable_address'}), on="persona_id", how='left')
# else:
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker_f, on="persona_id", how='left')

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# Info about personas experiences

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# info of all experiences[]
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_experiences = pl.DataFrame(conn.execute(query).df())


# info of all comapnies in said experiences
list_w = []
for word in df_all_personas_experiences['company_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT company_id, company_name, company_industry, company_linkedin_url, company_location_country
FROM read_parquet('{path_data_companies}')
WHERE company_id IN ({list_for_in})
"""
df_all_companies = pl.DataFrame(conn.execute(query).df())

df_full_personas_experiences_plus = df_all_personas_experiences.join(df_all_companies, on='company_id', how='left')

df_full_personas_experiences_plus = (
    df_full_personas_experiences_plus
    .with_columns(
        pl.when(
            pl.col('company_id')==current_company_id
        )
        .then(True)
        .otherwise(False)
        .alias('target')
    )
)

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# Info about personas education

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# info of all experiences
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_education}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_education = pl.DataFrame(conn.execute(query).df())


#ifon of allcomapnies in said experiences
list_w = []
for word in df_all_personas_education['school_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

if list_w:
    list_for_in = ', '.join(list_w)
    query = f"""
    SELECT school_id, school_name, school_type, school_website, school_location_country
    FROM read_parquet('{path_data_school}')
    WHERE school_id IN ({list_for_in})
    """
    df_all_school = pl.DataFrame(conn.execute(query).df())
    
    df_full_personas_education_plus = df_all_personas_education.join(df_all_school, on='school_id', how='left')
else:
    df_full_personas_education_plus = df_all_personas_education

1 About the project

The project came to life after realizing that web scraping doesn’t allow deep-level filtering—without consuming too much time.The irony is, this project itself took me about a month, but the final RDB contains more data than I could ever scrape.

The raw data was 1.4 TB in size and holds information previously scraped.
Processing was done on my local machine using Python, Polars, and DuckDB, following this workflow:
- Processed raw data into structured Parquet files using Polars.
- Transformed each Parquet file into mini RDBs using Polars.
- Merged all mini RDBs into one using DuckDB.
- Analyzed and filtered data to fit the current project.

Alt text

2 EDA

2.1 internet indestry’s yearly new recruit count

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list_of_unique_company_experience_years = []
for y in df_full_personas_who_worked_in_company['start_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)
for y in df_full_personas_who_worked_in_company['end_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)

list_year = []
list_state = []
list_count = []
list_names = []

def def_get_names_breked(tmp):
    if tmp.is_empty():
        names_string = ''
    else:
        tmp_list_name = []
        names_limit = 3
        row_limit = names_limit * 6
        for i, name in enumerate(tmp['full_name'].to_list()):
            ii = i+1
            tmp_list_name.append(name.title())
            if ii!=0 and ii%names_limit==0:
                tmp_list_name.append("<br>")
            if ii==row_limit:
                tmp_list_name.append("...")
                break
        names_string = ', '.join(tmp_list_name).replace(", <br>, ","<br>")
    return names_string

for y in list_of_unique_company_experience_years:
    #recuite state
    list_year.append(y)
    list_state.append('Recruited')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('start_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))
    
    #recuite state
    list_year.append(y)
    list_state.append('Resigned')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('end_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))

df_m_recruite_vs_resign = pl.DataFrame({
    'year':list_year,
    'status':list_state,
    'count':list_count,
    'names':list_names,})

2.2 smile open source solutions’s workforce status over the years

3 Persona company network graph

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gr_net = df_full_personas_experiences_plus.with_columns(pl.col('company_id').str.to_uppercase()).group_by('persona_id','company_id').agg(pl.len().alias('count')).sort('count')
list_top_in_network = gr_net['company_id'].value_counts().sort('count', descending=True)['company_id'].to_list()[:5]
gr_net_f = gr_net.filter(pl.col('company_id').is_in(list_top_in_network))

list_letters = ['A','B','C','D','E','F','G','H']
dict_company = {}
dict_company_rev = {}
for company, letter in zip(list_top_in_network, list_letters ):
    dict_company[letter] = company
    dict_company_rev[company] = letter

gr_gr_net_f = gr_net_f.sort('company_id').group_by('persona_id').agg(pl.col('company_id').unique().sort(),)

gr_gr_net_f2 = (
    gr_gr_net_f['company_id']
    .value_counts()
    .with_columns(
        # pl.col('company_id').list.join(', '),
        (pl.col('count')/len(gr_gr_net_f)).alias('per')
    )
    .sort('per',descending=True)
)

list_prob = []
for i in range(len(gr_gr_net_f2)):
    tmp_prob_letters = []
    for k in dict_company.keys():
        if dict_company[k] in gr_gr_net_f2[i]['company_id'][0].to_list():
            tmp_prob_letters.append(f' {k}')
        else:
            tmp_prob_letters.append(f'¬{k}')

    list_prob.append(f"P({' ∩ '.join(tmp_prob_letters)}) = {round(gr_gr_net_f2[i]['per'][0],4)}")
annon_prob_text = "<b>Probability Distribution:</b><br>" + '<br>'.join(list_prob)



# Create network graph
G = nx.Graph()
for persona, company in gr_net_f.select(['persona_id', 'company_id']).iter_rows():
    G.add_edge(persona, company)

# Get unique values
persona_ids = gr_net_f['persona_id'].unique().to_list()
company_ids = gr_net_f['company_id'].unique().to_list()

# Calculate degrees (connection counts)
degree_dict = dict(G.degree())

# Get min and max degrees for scaling
company_degrees = [degree_dict[c] for c in company_ids]
persona_degrees = [degree_dict[p] for p in persona_ids]

min_company_degree = min(company_degrees) if company_degrees else 1
max_company_degree = max(company_degrees) if company_degrees else 1
min_persona_degree = min(persona_degrees) if persona_degrees else 1
max_persona_degree = max(persona_degrees) if persona_degrees else 1

# Define size ranges
COMPANY_MIN_SIZE = 25
COMPANY_MAX_SIZE = 100
PERSONA_MIN_SIZE = 5
PERSONA_MAX_SIZE = 20

# print(f"Company connections range: {min_company_degree} - {max_company_degree}")
# print(f"Persona connections range: {min_persona_degree} - {max_persona_degree}")

# Sort companies by degree (size) in descending order
company_ids_sorted = sorted(company_ids, key=lambda x: degree_dict[x], reverse=True)

# Check if "Nokia" exists in the data
HIGHLIGHTED_COMPANY = current_company_id
HIGHLIGHTED_COMPANY_EXISTS = HIGHLIGHTED_COMPANY.lower() in [str(c).lower() for c in company_ids]

if HIGHLIGHTED_COMPANY_EXISTS:
    # Get the actual case-sensitive name
    highlighted_company = next(c for c in company_ids if str(c).lower() == HIGHLIGHTED_COMPANY.lower())
    # print(f"Highlighting company: {highlighted_company} (with {degree_dict[highlighted_company]} connections)")
else:
    # print(f"Warning: '{HIGHLIGHTED_COMPANY}' not found in company list")
    highlighted_company = None

# Create layout (companies on outer circle, ordered by size)
pos = {}
num_companies = len(company_ids_sorted)
radius_outer = 2.0

# Position companies on circle, ordered by size (largest first)
for i, company in enumerate(company_ids_sorted):
    # Start at top (90° or π/2 radians) and go counter-clockwise (add angle)
    # Counter-clockwise rotation: angle = start_angle + (i * 2π / num_companies)
    # This puts largest at top, next on left, then bottom, then right
    start_angle = np.pi / 2  # 90° at top
    
    # For counter-clockwise rotation
    angle = start_angle - (2 * np.pi * i / num_companies)
    
    # Convert to x, y coordinates
    pos[company] = (radius_outer * np.cos(angle), radius_outer * np.sin(angle))

# Position personas
for i, persona in enumerate(persona_ids):
    connected_companies = [c for c in company_ids if G.has_edge(persona, c)]
    if connected_companies:
        avg_x = np.mean([pos[c][0] for c in connected_companies])
        avg_y = np.mean([pos[c][1] for c in connected_companies])
        # Add jitter to spread out personas
        jitter_x = np.random.uniform(-0.2, 0.2)
        jitter_y = np.random.uniform(-0.2, 0.2)
        pos[persona] = (avg_x * 0.5 + jitter_x, avg_y * 0.5 + jitter_y)
    else:
        pos[persona] = (0, 0)

# Prepare edge traces
edge_x, edge_y = [], []
for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_x.extend([x0, x1, None])
    edge_y.extend([y0, y1, None])

edge_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.6, color='rgba(120, 120, 120, 0.15)'),
    hoverinfo='none',
    mode='lines')

# Prepare node traces with proportional sizing
company_x, company_y, company_text = [], [], []
company_color, company_size, company_hover = [], [], []
company_border_width = []  # For border thickness
company_border_color = []  # For border color

persona_x, persona_y = [], []
persona_color, persona_size, persona_hover = [], [], []

# Helper function to scale size proportionally
def scale_size(value, min_val, max_val, min_size, max_size):
    if max_val == min_val:
        return (min_size + max_size) / 2
    return min_size + (value - min_val) / (max_val - min_val) * (max_size - min_size)

# Add COMPANY nodes in sorted order (largest first)
for company in company_ids_sorted:
    x, y = pos[company]
    company_x.append(x)
    company_y.append(y)
    company_text.append(str(company))
    company_color.append('#EF553B')
    
    connections = degree_dict[company]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections, 
        min_company_degree, 
        max_company_degree,
        COMPANY_MIN_SIZE, 
        COMPANY_MAX_SIZE
    )
    company_size.append(scaled_size)
    
    # Custom border for highlighted company
    if highlighted_company and company == highlighted_company:
        company_border_width.append(4)  # Thicker border
        company_border_color.append('#000000')  # Black border
    else:
        company_border_width.append(1)
        company_border_color.append('#000000')
    
    # Hover text
    personas = gr_net_f.filter(pl.col('company_id') == company)['persona_id'].to_list()
    rank = company_ids_sorted.index(company) + 1
    hover_text = f"<b>Company #{rank}:</b> {company}<br>"
    hover_text += f"<b>Personas worked here:</b> {connections}<br>"
    hover_text += f"<b>Connection rank:</b> {rank}/{len(company_ids_sorted)}<br>"
    if connections > 0:
        for persona in personas[:5]:
            persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
            hover_text += f" • {persona_name}<br>"
        if connections > 5:
            hover_text += f" • ... and {connections - 5} more"
    company_hover.append(hover_text)

# Add PERSONA nodes
for persona in persona_ids:
    x, y = pos[persona]
    persona_x.append(x)
    persona_y.append(y)
    persona_color.append('#636efa')
    
    connections = degree_dict[persona]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections,
        min_persona_degree,
        max_persona_degree,
        PERSONA_MIN_SIZE,
        PERSONA_MAX_SIZE
    )
    persona_size.append(scaled_size)
    
    # Hover text
    companies = gr_net_f.filter(pl.col('persona_id') == persona)['company_id'].to_list()
    persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
    hover_text = f"<b>Persona:</b> {persona_name}<br>"
    hover_text += f"<b>Companies worked at:</b> {connections}<br>"
    if connections > 0:
        # Check if worked at highlighted company
        if highlighted_company:
            worked_at_highlighted = highlighted_company in companies
            if worked_at_highlighted:
                hover_text += f"<b>Worked at {highlighted_company}:</b> ✓<br>"
        
        hover_text += "<br>".join([f"  • {comp}" for comp in companies[:5]])
        if connections > 5:
            hover_text += f"<br>  • ... and {connections - 5} more"
    persona_hover.append(hover_text)

# Create company node trace
company_trace = go.Scatter(
    x=company_x, y=company_y,
    mode='markers+text',
    hoverinfo='text',
    hovertext=company_hover,
    text=company_text,
    textposition="top center",
    textfont=dict(size=14, color='black'),
    marker=dict(
        color=company_color,
        size=company_size,
        line=dict(
            width=company_border_width,
            color=company_border_color
        ),
        opacity=0.9)
)

# Create persona node trace
persona_trace = go.Scatter(
    x=persona_x, y=persona_y,
    mode='markers',
    hoverinfo='text',
    hovertext=persona_hover,
    text=None,  # No text for personas
    marker=dict(
        color=persona_color,
        size=persona_size,
        line=dict(width=1, color='black'),
        opacity=0.7)
)

# Calculate axis ranges for 1:1 aspect ratio
all_positions = list(pos.values())
x_vals = [p[0] for p in all_positions]
y_vals = [p[1] for p in all_positions]

# Add padding
x_range = [min(x_vals) - 0.5, max(x_vals) + 0.5]
y_range = [min(y_vals) - 0.5, max(y_vals) + 0.5]

# Make axes have the same range for 1:1 aspect
max_range = max(x_range[1] - x_range[0], y_range[1] - y_range[0])
x_center = (x_range[0] + x_range[1]) / 2
y_center = (y_range[0] + y_range[1]) / 2

x_range = [x_center - max_range/2, x_center + max_range/2]
y_range = [y_center - max_range/2, y_center + max_range/2]

# Create figure with 1:1 aspect ratio
fig = go.Figure(data=[edge_trace, persona_trace, company_trace],
                layout=go.Layout(
                    title=f'Persona-Company Network (Companies Ordered by Size)<br><sup>Highlighted: {highlighted_company if highlighted_company else "None"}</sup>',
                    showlegend=False,
                    hovermode='closest',
                    margin=dict(b=20, l=20, r=20, t=100),
                    xaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=x_range,
                        scaleanchor="y",
                        scaleratio=1
                    ),
                    yaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=y_range
                    ),
                    plot_bgcolor='white',
                    paper_bgcolor='white',
                    width=900,
                    height=900
                ))

# Add legend with size examples and highlighting info
# legend_text = f"""
# <b>Node Size = Connection Count</b><br>
# <span style='color:#EF553B'>● Companies</span><br>
# <span style='color:#636efa'>● Personas</span> (hover for details)
# """

# fig.add_annotation(
#     x=0.98, y=0.98,
#     xref="paper", yref="paper",
#     text=legend_text,
#     showarrow=False,
#     font=dict(size=14),
#     align="left",
#     bgcolor="rgba(255, 255, 255, 0.95)",
    
# )

# Add top companies list
top_companies = company_ids_sorted[:10]  # Top 10 companies
top_companies_text = "<b>Top Companies by Connections:</b><br>"
for i, company in enumerate(top_companies, 1):
    connections = degree_dict[company]
    top_connections = degree_dict[top_companies[0]]
    connections_per = f" | {round(connections/top_connections*100)}%" if highlighted_company and company != highlighted_company else ""
    highlight_indicator = " " if highlighted_company and company == highlighted_company else ""
    top_companies_text += f"{dict_company_rev[company]}. {company}: {connections} {connections_per} {highlight_indicator}<br>"

fig.add_annotation(
    x=0.02, y=0.98,
    xref="paper", yref="paper",
    text=top_companies_text,
    showarrow=False,
    font=dict(size=14),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.9)",
    # bordercolor="#666",
    # borderwidth=1
)

# Add probabiliy list

fig.add_annotation(
    x=0.98, y=0.98,
    xref="paper", yref="paper",
    text=annon_prob_text,
    showarrow=False,
    font=dict(
        family="'Courier New', monospace",  # Multiple fallbacks
        size=12,
        color="black"
    ),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.95)",
    
)
fig.write_image((path_output_images/f'network_{current_company_id}.webp'))
fig.show()

Show the code

amount = 5

tmp = df_full_personas_who_worked_in_company.sort(
    ["inferred_salary", "linkedin_connections", "inferred_years_experience"],
    descending=True,
)
tmp_gr = df_full_personas_experiences_plus.group_by('persona_id').agg(pl.len().alias('experience_count'))
tmp = df_full_personas_who_worked_in_company.join(tmp_gr, on='persona_id').sort('experience_count',descending=True)

tmp2 = pl.concat(
    [tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==True)[:amount*2],
     tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==False)[:amount*2],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==True)[:amount*1],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==False)[:amount*1],
     tmp.filter(pl.col('title_name').str.contains_any(['founder','ceo','presi','owner']))
    ]
).sort("full_name")

list_persona_for_plot = tmp2['persona_id'].to_list()

Show the code

# Workforce data

Show the code

def def_plotly_experience_range(current_persona_id):
    tmp_df = (df_full_personas_who_worked_in_company
              .filter(pl.col('persona_id')==current_persona_id)
              .with_columns(pl.col('end_year').fill_null(2021))['start_year','end_year'])
    
    fig_tmp = copy.deepcopy(fig_company_hiring_trend)
    fig_tmp.add_vrect(
        x0=tmp_df[0,'start_year'],
        x1=tmp_df[0,'end_year'],
        fillcolor="blue",
        opacity=0.1,
        line_width=0 
    )
    return fig_tmp

def def_plotly_experience_gantt(current_persona_id):
    px_data = (df_full_personas_experiences_plus
               .filter(pl.col('persona_id')==current_persona_id)
               .with_columns(
                   pl.col('end_date').fill_null(datetime.datetime(2020, 1, 1, 0,0)),
                   pl.col('company_name').str.to_uppercase(),
                   # pl.col('company_name').str.to_uppercase().str.replace_all('&', '-and-')
               )
               .sort('start_date'))
    
    y_order = px_data['company_name'].to_list()
    
    fig = px.timeline(px_data,x_start="start_date", x_end="end_date", y="company_name",
                      color='target',hover_data=["title_name"], height=140+30*len(px_data),
                      category_orders={"company_name": y_order},
                      color_discrete_map={True:'#EF553B',  False:'#636efa'},
                      labels={'target':'Target', 'start_date':'Recruited', 'end_date':'If-Resigned', 
                             'company_name':'Company', 'title_name':'Job role'}
                     # title=f"Experience of {current_persona_name}.",
                     )
    fig.update_yaxes(
        # autorange="reversed",
                              showgrid=True,
                              gridcolor='lightgray',
                              gridwidth=1,
                              griddash='dot'
    )
    fig.update_layout(showlegend=False, xaxis_title=None, yaxis_title=None)
    return fig

4 Workforce sample

4.1 Atef Zayati

Job title: Ingã nieur ã tudes et dã veloppement 2
Socials: https://vimeo.com/user/11359437 | https://linkedin.com/in/atef-zayati | https://linkedin.com/in/atef-zayati-7bb71156 | https://twitter.com/atef_zayati

4.1.1 Atef Zayati’s working period at smile open source solutions

4.1.2 Gantt plot of Atef Zayati’s experience

4.2 Audrey Aletti

Job title: Ingã nieur etude et dã veloppement
Socials: https://linkedin.com/in/audreyaletti

4.2.1 Audrey Aletti’s working period at smile open source solutions

4.2.2 Gantt plot of Audrey Aletti’s experience

4.3 Chika Ikeora

Job title: Business owner
Socials: https://linkedin.com/in/chika-ikeora-10b56311b

4.3.1 Chika Ikeora’s working period at smile open source solutions

4.3.2 Gantt plot of Chika Ikeora’s experience

4.4 Christophe D’Arras

Job title: Engagement manager, directeur de projet
Socials: https://linkedin.com/in/christophe-d-arras | https://linkedin.com/in/christophe-d-arras-a51b3a15

4.4.1 Christophe D’Arras’s working period at smile open source solutions

4.4.2 Gantt plot of Christophe D’Arras’s experience

4.5 Céline Juan

Job title: Intã grateur web
Socials: https://linkedin.com/in/celinejuan

4.5.1 Céline Juan’s working period at smile open source solutions

4.5.2 Gantt plot of Céline Juan’s experience

4.6 Dick Face

Job title: President
Socials: https://linkedin.com/in/dick-face-93774814

4.6.1 Dick Face’s working period at smile open source solutions

4.6.2 Gantt plot of Dick Face’s experience

4.7 Dominique Debailleux

Job title: Wso2 trainer
Socials: https://linkedin.com/in/dominique-debailleux-5338b28 | https://linkedin.com/in/dominiquedebailleux

4.7.1 Dominique Debailleux’s working period at smile open source solutions

4.7.2 Gantt plot of Dominique Debailleux’s experience

4.8 Erwin De Vries

Job title: Project manager
Socials: https://twitter.com/devries_it | https://linkedin.com/in/devrieserwin

4.8.1 Erwin De Vries’s working period at smile open source solutions

4.8.2 Gantt plot of Erwin De Vries’s experience

4.9 Fanny Declerck

Job title: Technician lead
Socials: https://linkedin.com/in/fanny-declerck-1879a589

4.9.1 Fanny Declerck’s working period at smile open source solutions

4.9.2 Gantt plot of Fanny Declerck’s experience

4.10 Florian Casagrande

Job title: Responsable des pã les stockage, sauvegarde et serveurs
Socials: https://linkedin.com/in/florian-haller-casagrande-394a1a53 | https://linkedin.com/in/florianhc

4.10.1 Florian Casagrande’s working period at smile open source solutions

4.10.2 Gantt plot of Florian Casagrande’s experience

4.11 Frédéric Beaupin

Job title: Dã veloppeur logiciels
Socials: https://linkedin.com/in/frederic-beaupin | https://linkedin.com/in/frã©dã©ric-beaupin-8b8a4445 | https://linkedin.com/in/frédéric-beaupin-8b8a4445

4.11.1 Frédéric Beaupin’s working period at smile open source solutions

4.11.2 Gantt plot of Frédéric Beaupin’s experience

4.12 Guillaume Outters

Job title: Dã veloppeur; expert
Socials: https://linkedin.com/in/guillaume-outters-4b8845ab

4.12.1 Guillaume Outters’s working period at smile open source solutions

4.12.2 Gantt plot of Guillaume Outters’s experience

4.13 Han Stiphout

Job title: Owner
Socials: https://linkedin.com/in/han-stiphout-19786a17 | https://linkedin.com/in/han-stiphout-24b2776

4.13.1 Han Stiphout’s working period at smile open source solutions

4.13.2 Gantt plot of Han Stiphout’s experience

4.14 Hugo Pascal

Job title: Lead technician junior ã smile bordeaux
Socials: https://linkedin.com/in/hugo-pascal-3a57a433 | https://twitter.com/hugo_pasc | https://github.com/pascalhugo | https://gravatar.com/pascalhugo16

4.14.1 Hugo Pascal’s working period at smile open source solutions

4.14.2 Gantt plot of Hugo Pascal’s experience

4.15 Jean-Laurent Fambon

Job title: Web consultant and global account manager
Socials: https://linkedin.com/in/jean-laurent-fambon-4baabb | https://twitter.com/jeanlaurentf | https://linkedin.com/in/jlfambon

4.15.1 Jean-Laurent Fambon’s working period at smile open source solutions

4.15.2 Gantt plot of Jean-Laurent Fambon’s experience

4.16 Jean-Philippe Dufey

Job title: Sales engineer
Socials: https://linkedin.com/in/jean-philippe-dufey-11a01017 | https://linkedin.com/in/jean-philippe-dufey-swiss

4.16.1 Jean-Philippe Dufey’s working period at smile open source solutions

4.16.2 Gantt plot of Jean-Philippe Dufey’s experience

4.17 Jean-Philippe Lefieux

Job title: Lead user experience designer
Socials: https://twitter.com/_kuasa_ | https://linkedin.com/in/jean-philippe-lefieux-9549688 | https://linkedin.com/in/jplefieux

4.17.1 Jean-Philippe Lefieux’s working period at smile open source solutions

4.17.2 Gantt plot of Jean-Philippe Lefieux’s experience

4.18 Jonathan Maerckaert

Job title: Manager
Socials: https://facebook.com/jmaerckaert | https://linkedin.com/in/jonathan-maerckaert-ab338312 | https://linkedin.com/in/jost-schweinfurther-ba508b8a

4.18.1 Jonathan Maerckaert’s working period at smile open source solutions

4.18.2 Gantt plot of Jonathan Maerckaert’s experience

4.19 Kevin Deldycke

4.19.1 Kevin Deldycke’s working period at smile open source solutions

4.19.2 Gantt plot of Kevin Deldycke’s experience

4.20 Liva Castanet

Job title: Lead technician magento 2
Socials: https://linkedin.com/in/liva-castanet-2766603a | https://linkedin.com/in/livacastanetfrontenddeveloper

4.20.1 Liva Castanet’s working period at smile open source solutions

4.20.2 Gantt plot of Liva Castanet’s experience

4.21 Ludivine Lenoir

Job title: Product owner - project manager
Socials: https://linkedin.com/in/ivylenoir | https://linkedin.com/in/ludivine-ivy-lenoir-87b54120

4.21.1 Ludivine Lenoir’s working period at smile open source solutions

4.21.2 Gantt plot of Ludivine Lenoir’s experience

4.22 Mathieu Betrancourt

Job title: Member of the supervisory board
Socials: https://linkedin.com/in/mathieu-betrancourt-a156643

4.22.1 Mathieu Betrancourt’s working period at smile open source solutions

4.22.2 Gantt plot of Mathieu Betrancourt’s experience

4.23 Maxime Topolov

Job title: Vice president strategic presales, comex member
Socials: https://foursquare.com/user/2840392 | https://meetup.com/members/64755502 | https://gravatar.com/drupalwordpress | https://quora.com/maxime-topolov | https://linkedin.com/in/maxime-topolov-aa2591 | https://facebook.com/mtopolov | https://linkedin.com/in/mtopolov | https://pinterest.com/mtopolov | https://twitter.com/mtopolov

4.23.1 Maxime Topolov’s working period at smile open source solutions

4.23.2 Gantt plot of Maxime Topolov’s experience

4.24 Mehdi Nagati

Job title: Business solutions business unit director
Socials: https://linkedin.com/in/mehdi-nagati-3004a61 | https://linkedin.com/in/mehdinagati

4.24.1 Mehdi Nagati’s working period at smile open source solutions

4.24.2 Gantt plot of Mehdi Nagati’s experience

4.25 Michael Fanini

Job title: Consultant digital and ebusiness and product owner
Socials: https://linkedin.com/in/michael-fanini-aaa87036 | https://linkedin.com/in/michaël-fanini-aaa87036

4.25.1 Michael Fanini’s working period at smile open source solutions

4.25.2 Gantt plot of Michael Fanini’s experience

4.26 Michaël Guillion

Job title: Senior engagement manager
Socials: https://linkedin.com/in/guillionmichael | https://twitter.com/mguillion | https://linkedin.com/in/michaël-guillion-3656a93

4.26.1 Michaël Guillion’s working period at smile open source solutions

4.26.2 Gantt plot of Michaël Guillion’s experience

4.27 Oleksandr Kruk

Job title: Magento developer
Socials: https://linkedin.com/in/oleksandr-kruk-6b371a97 | https://linkedin.com/in/oleksandrkruk

4.27.1 Oleksandr Kruk’s working period at smile open source solutions

4.27.2 Gantt plot of Oleksandr Kruk’s experience

4.28 Olivier Piffaut

Job title: Expert technique
Socials: https://linkedin.com/in/olivier-piffaut-9730251

4.28.1 Olivier Piffaut’s working period at smile open source solutions

4.28.2 Gantt plot of Olivier Piffaut’s experience

4.29 Pierrick Fischer

Job title: Product owner
Socials: https://linkedin.com/in/pierrick-fischer-57587716 | https://linkedin.com/in/pierrick-fischer-58087716 | https://linkedin.com/in/pierrickfischer

4.29.1 Pierrick Fischer’s working period at smile open source solutions

4.29.2 Gantt plot of Pierrick Fischer’s experience

4.30 Prasannah Baskaranathan

Job title: Product owner
Socials: https://linkedin.com/in/pbaskaranathan | https://linkedin.com/in/prasannah-baskaranathan-22215b106

4.30.1 Prasannah Baskaranathan’s working period at smile open source solutions

4.30.2 Gantt plot of Prasannah Baskaranathan’s experience

4.31 Quentin Hess

Job title: Chef de projet hosting
Socials: https://linkedin.com/in/quentinhess75

4.31.1 Quentin Hess’s working period at smile open source solutions

4.31.2 Gantt plot of Quentin Hess’s experience

4.32 Radouane Largui

Job title: Owner
Socials: https://linkedin.com/in/radouane-largui-6b96254

4.32.1 Radouane Largui’s working period at smile open source solutions

4.32.2 Gantt plot of Radouane Largui’s experience

4.33 Ram Krish

Job title: Vice president
Socials: https://linkedin.com/in/ram-krish-5164954

4.33.1 Ram Krish’s working period at smile open source solutions

4.33.2 Gantt plot of Ram Krish’s experience

4.34 Romain Chauveau

Job title: Technician lead
Socials: https://linkedin.com/in/romain-chauveau-0148897b

4.34.1 Romain Chauveau’s working period at smile open source solutions

4.34.2 Gantt plot of Romain Chauveau’s experience

4.35 Régis Mongrédien

Job title: Consultant ecommerce
Socials: https://linkedin.com/in/regismongredien

4.35.1 Régis Mongrédien’s working period at smile open source solutions

4.35.2 Gantt plot of Régis Mongrédien’s experience

4.36 Safaa Benadi

Job title: Product owner - qualitã logiciel
Socials: https://linkedin.com/in/safaa-benadi-426604124

4.36.1 Safaa Benadi’s working period at smile open source solutions

4.36.2 Gantt plot of Safaa Benadi’s experience

4.37 Sarah Merceille

Job title: Head of group controlling
Socials: https://linkedin.com/in/sarahmerceille

4.37.1 Sarah Merceille’s working period at smile open source solutions

4.37.2 Gantt plot of Sarah Merceille’s experience

4.38 Shannon Allard

Job title: Assistant director
Socials: https://linkedin.com/in/shannon-allard-93056322 | https://linkedin.com/in/shannon-k-allard-93056322 | https://twitter.com/skimberlyx

4.38.1 Shannon Allard’s working period at smile open source solutions

4.38.2 Gantt plot of Shannon Allard’s experience

4.39 Stéphane Heuzé

Job title: Expert technique drupal 7
Socials: https://linkedin.com/in/stã©phane-heuzã©-9bbbb9131 | https://linkedin.com/in/stéphane-heuzé-9bbbb9131

4.39.1 Stéphane Heuzé’s working period at smile open source solutions

4.39.2 Gantt plot of Stéphane Heuzé’s experience

4.40 Thierry Fontaine

Job title: Product owner
Socials: https://linkedin.com/in/thierry-fontaine-6a80a438

4.40.1 Thierry Fontaine’s working period at smile open source solutions

4.40.2 Gantt plot of Thierry Fontaine’s experience

4.41 Thomas Pelletier

Job title: Product owner
Socials: https://linkedin.com/in/thomas-pelletier-0b594b34 | https://linkedin.com/in/thomas-pelletier-pm

4.41.1 Thomas Pelletier’s working period at smile open source solutions

4.41.2 Gantt plot of Thomas Pelletier’s experience

4.42 Yannick Quenec’Hdu

4.42.1 Yannick Quenec’Hdu’s working period at smile open source solutions

4.42.2 Gantt plot of Yannick Quenec’Hdu’s experience

4.43 Yasser Moudouani

Job title: Technician lead
Socials: https://linkedin.com/in/yasser-moudouani-603203106

4.43.1 Yasser Moudouani’s working period at smile open source solutions

4.43.2 Gantt plot of Yasser Moudouani’s experience

4.44 Yassine Teimi

Job title: Odoo project manager and product owner
Socials: https://linkedin.com/in/yassine-teimi-9b344541

4.44.1 Yassine Teimi’s working period at smile open source solutions

4.44.2 Gantt plot of Yassine Teimi’s experience

Show the code

df_full_personas_who_worked_in_company.write_parquet(current_company_parquet)